Learning semantic sentence representations from visually grounded language without lexical knowledge

Current approaches to learning semantic representations of sentences often use prior word-level knowledge. The current study aims to leverage visual information in order to capture sentence level semantics without the need for word embeddings. We use a multimodal sentence encoder trained on a corpus of images with matching text captions to produce visually grounded sentence embeddings. Deep Neural Networks are trained to map the two modalities to a common embedding space such that for an image the corresponding caption can be retrieved and vice versa. We show that our model achieves results comparable to the current state-of-the-art on two popular image-caption retrieval benchmark data sets: MSCOCO and Flickr8k. We evaluate the semantic content of the resulting sentence embeddings using the data from the Semantic Textual Similarity benchmark task and show that the multimodal embeddings correlate well with human semantic similarity judgements. The system achieves state-of-the-art results on several of these benchmarks, which shows that a system trained solely on multimodal data, without assuming any word representations, is able to capture sentence level semantics. Importantly, this result shows that we do not need prior knowledge of lexical level semantics in order to model sentence level semantics. These findings demonstrate the importance of visual information in semantics

Semantic textual similarity with siamese neural networks

Calculating the Semantic Textual Similarity (STS) is an important research area in natural language processing which plays a significant role in many applications such as question answering, document summarisation, information retrieval and information extraction. This paper evaluates Siamese recurrent architectures, a special type of neural networks, which are used here to measure STS. Several variants of the architecture are compared with existing method

Textual Entailment Recognition with Semantic Features from Empirical Text Representation

Textual entailment recognition is one of the basic natural language understanding(NLU) tasks. Understanding the meaning of sentences is a prerequisite before applying any natural language processing(NLP) techniques to automatically recognize the textual entailment. A text entails a hypothesis if and only if the true value of the hypothesis follows the text. Classical approaches generally utilize the feature value of each word from word embedding to represent the sentences. In this paper, we propose a novel approach to identifying the textual entailment relationship between text and hypothesis, thereby introducing a new semantic feature focusing on empirical threshold-based semantic text representation. We employ an element-wise Manhattan distance vector-based feature that can identify the semantic entailment relationship between the text-hypothesis pair. We carried out several experiments on a benchmark entailment classification(SICK-RTE) dataset. We train several machine learning(ML) algorithms applying both semantic and lexical features to classify the text-hypothesis pair as entailment, neutral, or contradiction. Our empirical sentence representation technique enriches the semantic information of the texts and hypotheses found to be more efficient than the classical ones. In the end, our approach significantly outperforms known methods in understanding the meaning of the sentences for the textual entailment classification task.Comment: Pre-print for our paper at International Conference on Speech & Language Technology for Low-resource Languages (SPELLL'2022

SemEval-2017 Task 1: semantic textual similarity - multilingual and cross-lingual focused evaluation

Semantic Textual Similarity (STS) measures the meaning similarity of sentences. Applications include machine translation (MT), summarization, generation, question answering (QA), short answer grading, semantic search, dialog and conversational systems. The STS shared task is a venue for assessing the current state-of-the-art. The 2017 task focuses on multilingual and cross-lingual pairs with one sub-track exploring MT quality estimation (MTQE) data. The task obtained strong participation from 31 teams, with 17 participating in all language tracks. We summarize performance and review a selection of well performing methods. Analysis highlights common errors, providing insight into the limitations of existing models. To support ongoing work on semantic representations, the STS Benchmark is introduced as a new shared training and evaluation set carefully selected from the corpus of English STS shared task data (2012-2017)

Hizkuntza-ulermenari ekarpenak: N-gramen arteko atentzio eta lerrokatzeak antzekotasun eta inferentzia interpretagarrirako.

148 p.Hizkuntzaren Prozesamenduaren bitartez hezkuntzaren alorreko sistemaadimendunak hobetzea posible da, ikasleen eta irakasleen lan-karganabarmenki arinduz. Tesi honetan esaldi-mailako hizkuntza-ulermena aztertueta proposamen berrien bitartez sistema adimendunen hizkuntza-ulermenaareagotzen dugu, sistemei erabiltzailearen esaldiak modu zehatzagoaninterpretatzeko gaitasuna emanez. Esaldiak modu finean interpretatzekogaitasunak feedbacka modu automatikoan sortzeko aukera ematen baitu.Tesi hau garatzeko hizkuntza-ulermenean sakondu dugu antzekotasunsemantikoari eta inferentzia logikoari dagokien ezaugarriak eta sistemakaztertuz. Bereziki, esaldi barneko hitzak multzotan egituratuz eta lerrokatuzesaldiak hobeto modelatu daitezkeela erakutsi dugu. Horretarako, hitz solteaklerrokatzen dituen aurrekarien egoerako neurona-sare sistema batinplementatu eta n-grama arbitrarioak lerrokatzeko moldaketak egin ditugu.Hitzen arteko lerrokatzea aspalditik ezaguna bada ere, tesi honek, lehen aldiz,n-grama arbitrarioak atentzio-mekanismo baten bitartez lerrokatzekoproposamenak plazaratzen ditu.Gainera, esaldien arteko antzekotasunak eta desberdintasunak moduzehatzean identifikatzeko, esaldien interpretagarritasuna areagotzeko etaikasleei feedback zehatza emateko geruza berri bat sortu dugu: iSTS.Antzekotasun semantikoa eta inferentzia logikoa biltzen dituen geruzahorrekin chunkak lerrokatu ditugu, eta ikasleei feedback zehatza emateko gaiizan garela frogatu dugu hezkuntzaren testuinguruko bi ebaluazioeszenariotan.Tesi honekin batera hainbat sistema eta datu-multzo argitaratu diraetorkizunean komunitate zientifikoak ikertzen jarrai dezan

Intelligent Translation Memory Matching and Retrieval with Sentence Encoders

Matching and retrieving previously translated segments from a Translation Memory is the key functionality in Translation Memories systems. However this matching and retrieving process is still limited to algorithms based on edit distance which we have identified as a major drawback in Translation Memories systems. In this paper we introduce sentence encoders to improve the matching and retrieving process in Translation Memories systems - an effective and efficient solution to replace edit distance based algorithms.Comment: Accepted to EAMT 202

Selecting and Generating Computational Meaning Representations for Short Texts

Language conveys meaning, so natural language processing (NLP) requires representations of meaning. This work addresses two broad questions: (1) What meaning representation should we use? and (2) How can we transform text to our chosen meaning representation? In the first part, we explore different meaning representations (MRs) of short texts, ranging from surface forms to deep-learning-based models. We show the advantages and disadvantages of a variety of MRs for summarization, paraphrase detection, and clustering. In the second part, we use SQL as a running example for an in-depth look at how we can parse text into our chosen MR. We examine the text-to-SQL problem from three perspectives—methodology, systems, and applications—and show how each contributes to a fuller understanding of the task.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143967/1/cfdollak_1.pd

Computational models for semantic textual similarity

164 p.The overarching goal of this thesis is to advance on computational models of meaning and their evaluation. To achieve this goal we define two tasks and develop state-of-the-art systems that tackle both task: Semantic Textual Similarity (STS) and Typed Similarity.STS aims to measure the degree of semantic equivalence between two sentences by assigning graded similarity values that capture the intermediate shades of similarity. We have collected pairs of sentences to construct datasets for STS, a total of 15,436 pairs of sentences, being by far the largest collection of data for STS.We have designed, constructed and evaluated a new approach to combine knowledge-based and corpus-based methods using a cube. This new system for STS is on par with state-of-the-art approaches that make use of Machine Learning (ML) without using any of it, but ML can be used on this system, improving the results.Typed Similarity tries to identify the type of relation that holds between a pair of similar items in a digital library. Providing a reason why items are similar has applications in recommendation, personalization, and search. A range of types of similarity in this collection were identified and a set of 1,500 pairs of items from the collection were annotated using crowdsourcing.Finally, we present systems capable of resolving the Typed Similarity task. The best system resulted in a real-world application to recommend similar items to users in an online digital library